Commentators have already gotten hung-up on whether English became simplified before or after spreading, but this misses the impact of the article: There is an alternative approach to linguistics which looks at the differences between languages and recognises social factors as the primary source of linguistic change. Furthermore, these ideas are testable using statistics and genetic methods. It’s a pity the article didn’t mention the possibility of experimental approaches, including Gareth Roberts’ work on emerging linguistic diversity and work on cultural transmission using the Pictionary paradigm (Simon Garrod, Nick Fay, Bruno Gallantucci, see here and here).

David Robson (2011). Power of Babel: Why one language isn’t enough New Scientist, 2842Online

Replicated Typo 2.0 has reached 100,000 hits! The most popular search term that leads visitors here is ‘What makes humans unique?’ and part of the answer has to be our ability to transmit our culture. But as we’ve shown on this blog, culturally transmitted features can be highly correlated with each other. This fact is a source of both frustration and fascination, so I’ve roped together some of my favourite investigations of cultural correlations into a correlation super-chain. In addition, there’s a whole new spurious correlation at the end of the article!

Last week I put up a link to an online experiment. Here’s the results! You can still do the experiment first, if you like, here. Source code and raw results at the bottom.

Languages evolve over time under a pressure to be learned by a new generation. Does learning two languages at once effect this pressure? My experiment says … maybe.

These pressures include ones for learnability (compression) and expressivity (able to express a large variety of meanings, Kirby, Cornish & Smith, 2008). Bilingualism seems like an unlikely ability since learning an extra language leaves the speaker potentially no more expressive at a cost of an increase in the amount of effort required to learn it. There is no pressure for one language structure (e.g. English) to adapt to another language (e.g. Mandarin) so that they can become optimally learnable and expressive as a single medium. That is, there’s no reason to assume that expressivity and learnability pressures apply across languages (which are not being used by the same people).

Nevertheless, children display an aptitude and a willingness to learn and use multiple languages simultaneously, and at a similar rate to monolingual children. Therefore, languages do seem to have adapted to be learnable simultaneously. Does the compatibility of languages point to a strong innate property of language? In contrast, it might point to underlying similarity in the structure of languages, brought about by universal principles of communication.

You can take part in a pilot experiment about language learning: It takes about 8 minutes (and is NOT an iterated learning experiment, although it looks a bit like one). I’ll release the results (and the hypothesis) right here on Replicated Typo.

Woah, I just read some of the responses to Dunn et al. (2011) “Evolved structure of language shows lineage-specific trends in word-order universals” (language log here, Replicated Typo coverage here). It’s come in for a lot of flack. One concern raised at the LEC was that, considering an extreme interpretation, there may be no affect of universal biases on language structure. This goes against Generativist approaches, but also the Evolutionary approach adopted by LEC-types. For instance, Kirby, Dowman & Griffiths (2007) suggest that there are weak universal biases which are amplified by culture. But there should be some trace of universality none the less.

Below is the relationship diagram for Indo-European and Uto-Aztecan feature dependencies from Dunn et al.. Bolder lines indicate stronger dependencies. They appear to have different dependencies- only one is shared (Genitive-Noun and Object-Verb).

However, I looked at the median Bayes Factors for each of the possible dependencies (available in the supplementary materials). These are the raw numbers that the above diagrams are based on. If the dependencies’ strength rank in roughly the same order, they will have a high Spearman rank correlation.

Spearman Rank Correlation

Indo-European

Austronesian

Uto-Aztecan

0.39, p = 0.04

0.25, p = 0.19

Indo-European

-0.13, p = 0.49

Spearman rank correlation coefficients and p-values for Bayes Factors for different dependency pairs in different language families. Bantu was excluded because of missing feature data.

Although the Indo-European and Uto-Aztecan families have different strong dependencies, have similar rankings of those dependencies. That is, two features with a weak dependency in an Indo-European language tend to have a weak dependency in Uto-Aztecan language, and the same is true of strong dependencies. The same is true to some degree for Uto-Aztecan and Austronesian languages. This might suggest that there are, in fact, universal weak biases lurking beneath the surface. Lucky for us.

However, this does not hold between Indo-European and Austronesian language families. Actually, I have no idea whether a simple correlation between Bayes Factors makes any sense after hundreds of computer hours of advanced phylogenetic statistics, but the differences may be less striking than the diagram suggests.

UPDATE:

As Simon Greenhill points out below, the statistics are not at all conclusive. However, I’m adding the graphs for all Bayes Factors (these are made directly from the Bayes Factors in the Supplementary Material):

Replicated Typo is, as the name suggests, interested in transmission and change of cultural phenomena. I’m also particularly interested in bilingualism. That’s why I have to point out my recent discovery at Cognição, Linguagem e Música: A post by me, in Portuguese.

Well, more accurately, Pedro Lourenço Gomes has translated one of my recent articles. It’s fascinating that some of my thoughts may reach people with whom I could not communicate directly. Here’s an extract:

Original: There is a battle about to commence. A battle in the world of cognitive modelling. Or at least a bit of a skirmish. Two articles to be published in Trends in Cognitive Sciences debate the merits of approaching cognition from different ends of the microscope.

Actually, it looks like Google translate has done an OK job, although I don’t know anything about Portuguese. I had a look for more translations of Replicated Typo posts by searching for “Replicated Typo” with various language filters. Alas, I could find nothing.

If you like wading through deposits of dead animal material, then you should go over and visit Richard Littauer’s new blog, The Bog. Having been exposed to his writings on both thisblog, and through the Edinburgh language society website, I’m sure it will be worth a visit — for good writing, if not for your dire need to distinguish between forest swamps and shrub swamps. His first post is on Mung, the colloquial name for Pylaiella littoris, which is apparently a common seaweed. Here is his quick overview of the blog:

So, The Bog is going to be the resting place for various studies and explorations. Richard Littauer is the writer; he is working on his MA in Linguistics at Edinburgh University. He writes about evolutionary linguistics and culture at Replicated Typo, about general linguistic musings at a non-academic standard at Lang. Soc., about constructed languages on Llama, and about various philosophical things at Pitch Black Press. Since none of these blogs were a perfect fit for the scientific equivalent of a swamp-romp through subjects he doesn’t study, he set up this blog. Expect posts about ecology, biology, linguistics, anthropology, or anything in between.

The fact that it’s called the Bog has nothing to do with the British slang for ‘bathroom’. Rather, Richard (well, I) have an affinity with swamps for some unexplained reasons. Expect posts on swamps.

In a recent article covered in NatureNews in Societes Evolve in Steps, Tom Currie of UCL, and others, like Russell Gray of Auckland, use quantitative analysis of the Polynesian language group to plot socioanthropological movement and power hierarchies in Polynesia. This is based off of previous work, available here, which I saw presented at the Language as an Evolutionary Systemconference last July. The article claims that the means of change for political complexity can be determined using linguistic evidence in Polynesia, along with various migration theories and archaeological evidence.

I have my doubts.

Note: Most of the content in this post is refuted wonderfully in the comment section by one of the original authors of the paper. I highly recommend reading the comments, if you’re going to read this at all – that’s where the real meat lies. I’m keeping this post up, finally, because it’s good to make mistakes and learn from them. -Richard

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I had posted this already on the Edinburgh Language Society blog. I’ve edited it a bit for this blog. I should also state that this is my inaugural post on Replicated Typo; thanks to Wintz’ invitation, I’ll be posting here every now and again. It’s good to be here. Thanks for reading – and thanks for pointing out errors, problems, corrections, and commenting, if you do. Research blogging is relatively new to me, and I relish this unexpected chance to hone my skills and learn from my mistakes. (Who am I, anyway?) But without further ado:

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In a recent article covered in NatureNews in Societes Evolve in Steps, Tom Currie of UCL, and others, like Russell Gray of Auckland, use quantitative analysis of the Polynesian language group to plot socioanthropological movement and power hierarchies in Polynesia. This is based off of previous work, available here, which I saw presented at the Language as an Evolutionary Systemconference last July. The article claims that the means of change for political complexity can be determined using linguistic evidence in Polynesia, along with various migration theories and archaeological evidence.

I have my doubts. The talk that was given by Russell Gray suggested that there were still various theories about the migratory patterns of the Polynesians – in particular, where they started from. What his work did was to use massive supercomputers to narrow down all of the possibilities, by using lexicons and charting their similarities. The most probable were then recorded, and their statistical probability indicated what was probably the course of action. This, however, is where the ability for guessing ends. Remember, this is massive quantificational statistics. If one has a 70% probability chance of one language being the root of another, that isn’t to say that that language is the root, much less that the organisation of one determines the organisation of another. But statistics are normally unassailable – I only bring up this disclaimer because there isn’t always clear mapping between language usage and migration.